For the case Example REF , the dimensions of \(A\)  and \(B\)  are \(1000 \times 100\)  and \(4000 \times 100\) , respectively. Setting \(n = 70\) , we have that the rank of \(A\)  and \(B\)  are 30, respectively. Setting the parameters \(p = 60\)  and \(k = 20\) , we generate data sets of four different levels of results and make a comparison between our QiSLS algorithm and SVD algorithm. In Figure REF  and REF , the red dashed line denotes the results of statistical leverage scores (SLS) \(\ell _{i}\)  by SVD algorithm, and the black dot denotes the results of approximate statistical leverage scores \(\tilde{\ell }_{i}\)  by QiSLS algorithm, respectively. In Figure REF  and REF , denote the absolute error as \(\epsilon = |\ell _{i} - \tilde{\ell }_{i}|\) , the four colors \(\epsilon \)  represent the corresponding data errors. From Figure REF -REF , without loss of generality, for \(i \in [m]\) , we have that the statistical leverage scores \(\tilde{\ell }_{i}\)  computed by QiSLS algorithm can make a better approximation on \(\ell _{i}\) . Similar to previous work in [1]}, [2]}, [3]}, [4]} , we notice that the practical parameter \(p\)  is much smaller than the theoretical one. Just as [4]}, the performance of quantum inspired algorithm is better than the representations of theoretical complexity bounds.
